import disparity_filter_weighted_graphs as dfil
import networkx as nx

# Load the graph from a weighted edgelist.
graph = nx.read_weighted_edgelist('path_to_file', create_using=nx.Graph())

# Compute the 'alpha' value for each edge.
dfil.compute_alpha(graph)

# Find the optimal value for alpha. The dataframe used to find the optimal
#   value for alpha is saved to `finding_optimal_alpha.csv.zip`.
dfil.find_optimal_alpha(graph, save_optimal_alpha_data=True, method='elbow')

# Plot the position of the optimal value for alpha.
dfil.plot_optimal_alpha(graph)

# Create a filtered version of the original graph using the optimal value for alpha.
backbone = dfil.filter_graph(graph)